BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.6.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news1457@dmi.unibas.ch DTSTAMP;TZID=Europe/Zurich:20230309T105739 DTSTART;TZID=Europe/Zurich:20230427T161500 SUMMARY:Perlen-Colloquium: Dr. Frank Bretz (Novartis) DESCRIPTION:We consider the problem of testing multiple null hypotheses\, w here a decision to reject or retain must be made for each one and embeddin g incorrect decisions into a real-life context may inflict different losse s. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise err or rate may not be appropriate. Using a decision-theoretic approach\, we d efine suitable loss functions for a given decision rule\, where incorrect decisions can be treated unequally by assigning different loss values. Tak ing expectation with respect to the sampling distribution of the data allo ws us to control the familywise expected loss instead of the conventional familywise error rate. Different loss functions can be adopted\, and we se arch for decision rules that satisfy certain optimality criteria within a broad class of decision rules for which the expected loss is bounded by a fixed threshold under any parameter configuration. We illustrate the metho ds with the problem of establishing efficacy of a new medicinal treatment in non-overlapping subgroups of patients. X-ALT-DESC:

We consider the problem of testing multiple null hypotheses\, where a decision to reject or retain must be made for each one and embedd ing incorrect decisions into a real-life context may inflict different los ses. We argue that traditional methods controlling the Type I error rate m ay be too restrictive in this situation and that the standard familywise e rror rate may not be appropriate. Using a decision-theoretic approach\, we define suitable loss functions for a given decision rule\, where incorrec t decisions can be treated unequally by assigning different loss values. T aking expectation with respect to the sampling distribution of the data al lows us to control the familywise expected loss instead of the conventiona l familywise error rate. Different loss functions can be adopted\, and we search for decision rules that satisfy certain optimality criteria within a broad class of decision rules for which the expected loss is bounded by a fixed threshold under any parameter configuration. We illustrate the met hods with the problem of establishing efficacy of a new medicinal treatmen t in non-overlapping subgroups of patients.

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